Machining is as old as humanity, and changes in temperature in both the machine’s internal and external environments can be of great concern as they affect the machine’s thermal stability and, thus, the machine’s d...Machining is as old as humanity, and changes in temperature in both the machine’s internal and external environments can be of great concern as they affect the machine’s thermal stability and, thus, the machine’s dimensional accuracy. This paper is a continuation of our earlier work, which aimed to analyze the effect of the internal temperature of a machine tool as the machine is put into operation and vary the external temperature, the machine floor temperature. Some experiments are carried out under controlled conditions to study how machine tool components get heated up and how this heating up affects the machine’s accuracy due to thermally induced deviations. Additionally, another angle is added by varying the machine floor temperature. The parameters mentioned above are explored in line with the overall thermal stability of the machine tool and its dimensional accuracy. A Robodrill CNC machine tool is used. The CNC was first soaked with thermal energy by gradually raising the machine floor temperature to a certain level before putting the machine in operation. The machine was monitored, and analytical methods were deplored to evaluate thermal stability. Secondly, the machine was run idle for some time under raised floor temperature before it was put into operation. Data was also collected and analyzed. It is observed that machine thermal stability can be achieved in several ways depending on how the above parameters are joggled. This paper, in conclusion, reinforces the idea of machine tool warm-up process in conjunction with a carefully analyzed and established machine floor temperature variation for the approximation of the machine tool’s thermally stability to map the long-time behavior of the machine tool.展开更多
In all machining processes, tool wear is a natural phenomenon and it leads to tool failure. The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inhere...In all machining processes, tool wear is a natural phenomenon and it leads to tool failure. The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inherently produces high cutting temperature, which not only reduces tool life but also impairs the product quality. Metal cutting fluid changes the performance of machining operations because of their lubrication, cooling and chip flushing functions, but the use of cutting fluid has become more problematic in terms of both employee health and environmental pollution. The minimization of cutting fluid also leads to economical benefits by way of saving lubricant costs and workpiece/tool/machine cleaning cycle time. The concept of minimum quantity lubrication (MQL) has been suggested since a decade ago as a means of addressing the issues of environmental intru- siveness and occupational hazards associated with the airborne cutting fluid particles on factory shop floors. This paper deals with experimental investigation on the role of MQL by vegetable oil on cutting temperature, tool wear, surface roughness and dimen- sional deviation in turning AISI-1060 steel at industrial speed-feed combinations by uncoated carbide insert. The encouraging results include significant reduction in tool wear rate, dimensional inaccuracy and surface roughness by MQL mainly through reduction in the cutting zone temperature and favorable change in the chip-tool and work-tool interaction.展开更多
The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally in...The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.展开更多
针对同一工况下不同刀具磨损预测建模中的数据分布不同,从而导致的历史模型失效问题,提出了一种基于多通道一维卷积神经网络的刀具磨损动态预测建模方法。历史刀具磨损数据训练的多通道一维卷积神经网络,作为初始的刀具磨损预测历史模...针对同一工况下不同刀具磨损预测建模中的数据分布不同,从而导致的历史模型失效问题,提出了一种基于多通道一维卷积神经网络的刀具磨损动态预测建模方法。历史刀具磨损数据训练的多通道一维卷积神经网络,作为初始的刀具磨损预测历史模型。最大均值差异(maximum mean difference, MMD)法对不同刀具磨损数据进行相似度检测,当相似度相差较大时,在历史模型的基础上进行迭代更新,更新后的模型再对磨损数据进行预测。铣削试验验证结果表明,该方法能够准确预测不同刀具的磨损值大小,具有较好的自适应能力。展开更多
文摘Machining is as old as humanity, and changes in temperature in both the machine’s internal and external environments can be of great concern as they affect the machine’s thermal stability and, thus, the machine’s dimensional accuracy. This paper is a continuation of our earlier work, which aimed to analyze the effect of the internal temperature of a machine tool as the machine is put into operation and vary the external temperature, the machine floor temperature. Some experiments are carried out under controlled conditions to study how machine tool components get heated up and how this heating up affects the machine’s accuracy due to thermally induced deviations. Additionally, another angle is added by varying the machine floor temperature. The parameters mentioned above are explored in line with the overall thermal stability of the machine tool and its dimensional accuracy. A Robodrill CNC machine tool is used. The CNC was first soaked with thermal energy by gradually raising the machine floor temperature to a certain level before putting the machine in operation. The machine was monitored, and analytical methods were deplored to evaluate thermal stability. Secondly, the machine was run idle for some time under raised floor temperature before it was put into operation. Data was also collected and analyzed. It is observed that machine thermal stability can be achieved in several ways depending on how the above parameters are joggled. This paper, in conclusion, reinforces the idea of machine tool warm-up process in conjunction with a carefully analyzed and established machine floor temperature variation for the approximation of the machine tool’s thermally stability to map the long-time behavior of the machine tool.
基金Project (No. DEARS/CASR/R-01/2001/D-934 (30)) supported by Directorate of Advisory Extension and Research Services (DAERS), Committee for Advanced Studies & Research (CASR), BUET, Dhaka, Bangladesh
文摘In all machining processes, tool wear is a natural phenomenon and it leads to tool failure. The growing demands for high productivity of machining need use of high cutting velocity and feed rate. Such machining inherently produces high cutting temperature, which not only reduces tool life but also impairs the product quality. Metal cutting fluid changes the performance of machining operations because of their lubrication, cooling and chip flushing functions, but the use of cutting fluid has become more problematic in terms of both employee health and environmental pollution. The minimization of cutting fluid also leads to economical benefits by way of saving lubricant costs and workpiece/tool/machine cleaning cycle time. The concept of minimum quantity lubrication (MQL) has been suggested since a decade ago as a means of addressing the issues of environmental intru- siveness and occupational hazards associated with the airborne cutting fluid particles on factory shop floors. This paper deals with experimental investigation on the role of MQL by vegetable oil on cutting temperature, tool wear, surface roughness and dimen- sional deviation in turning AISI-1060 steel at industrial speed-feed combinations by uncoated carbide insert. The encouraging results include significant reduction in tool wear rate, dimensional inaccuracy and surface roughness by MQL mainly through reduction in the cutting zone temperature and favorable change in the chip-tool and work-tool interaction.
文摘The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.
文摘针对同一工况下不同刀具磨损预测建模中的数据分布不同,从而导致的历史模型失效问题,提出了一种基于多通道一维卷积神经网络的刀具磨损动态预测建模方法。历史刀具磨损数据训练的多通道一维卷积神经网络,作为初始的刀具磨损预测历史模型。最大均值差异(maximum mean difference, MMD)法对不同刀具磨损数据进行相似度检测,当相似度相差较大时,在历史模型的基础上进行迭代更新,更新后的模型再对磨损数据进行预测。铣削试验验证结果表明,该方法能够准确预测不同刀具的磨损值大小,具有较好的自适应能力。
